Today, the field of Artificial Intelligence (AI) is in a similar
situation as computer vision and robotics were years ago. We have a
large repertoire of mature methods and algorithms, but no standard
way to share them in a reusable format and no easy way to compose them
into complex solutions. Some effective frameworks do exist that allow
the modularization and composition of machine learning components,
including Keras [1]}, PyTorch [2]},
Tensorflow [3]} and Acumos [4]}. These,
however, are geared toward the use of data-driven, reactive
machine-learning components that are typically connected into simple,
linear pipelines.
